code: def generate_data() -> HeteroData:
      data = HeteroData()
      data["p"].x = torch.ones(5)
      data["a"].x = torch.ones(6)
      data["c"].x = torch.ones(3)
      data["p", "p"].edge_index = tensor([[0, 1, 2, 3], [1, 2, 4, 2]])
      data["p", "a"].edge_index = tensor(
          [[0, 1, 2, 3, 4], [2, 2, 5, 2, 5]]
      )
      data["a", "p"].edge_index = data["p", "a"].edge_index.flip([0])
      data["c", "p"].edge_index = tensor(
          [[0, 0, 1, 2, 2], [0, 1, 2, 3, 4]]
      )
      data["p", "c"].edge_index = data["c", "p"].edge_index.flip([0])
      return data
  
file_path: pytorch_geometric/test/transforms/test_add_metapaths.py
imports: import torch
  from torch import tensor
  from torch_geometric.data import HeteroData
  from torch_geometric.transforms import AddMetaPaths, AddRandomMetaPaths
  from torch_geometric.utils import coalesce
label_desc: Use image node_id 1 for calling a global function with example usage: generate_data() and returns: data